Machine learning-based predictive models for perioperative major adverse cardiovascular events in patients with stable coronary artery disease undergoing noncardiac surgery.

Background And Objective: Accurate prediction of perioperative major adverse cardiovascular events (MACEs) is crucial, as it not only aids clinicians in comprehensively assessing patients' surgical risks and tailoring personalized surgical and perioperative management plans, but also for information-based shared decision-making with patients and efficient allocation of medical resources. This study developed and validated a machine learning (ML) model using accessible preoperative clinical data to predict perioperative MACEs in stable coronary artery disease (SCAD) patients undergoing noncardiac surgery (NCS).

Methods: We collected data from 9171 adult SCAD patients who underwent NCS and extracted 64 preoperative variables.

Prognostic and predictive value of the novel classification of lung adenocarcinoma in patients with stage IB.

Purpose: Controversy remains exist for the effect of adjuvant chemotherapy (ACT) among stage IB lung adenocarcinoma patients. This study aimed to investigate the predictive value of the current lung adenocarcinoma classification system on benefit of ACT among patients with stage IB lung adenocarcinoma.

Methods: A total of 928 pathological stage IB invasive adenocarcinoma patients with R0 resection were included in this study.